Guang Sun1, Jingjing Lin1,*, Chen Yang1, Xiangyang Yin1, Ziyu Li1, Peng Guo1,2, Junqi Sun3, Xiaoping Fan1, Bin Pan1
Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189
Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >
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